Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42866
Title: Experiments and reference models in training neural networks for short-term wind power forecasting in electricity markets
Authors: Mendez, Juan 
Lorenzo, Javier 
Hernández, Mario 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Energía eólica
Redes neuronales
Issue Date: 2009
Project: Tecnicas de Visión Para la Interacción en Entornos de Interior Con Elaboración Mapas Cognitivos en Sistemas Perceptuales Heterogéneos. 
Journal: Lecture Notes in Computer Science 
Conference: 10th International Work-Conference on Artificial Neural Networks (IWANN 2009) 
10th International Work-Conference on Artificial Neural Networks, IWANN 2009 
Abstract: Many published studies in wind power forecasting based on Neural Networks have provided performance factors based on error criteria. Based on the standard protocol for forecasting, the published results must provide improvement criteria over the persistence or references models of its same place. Persistence forecasting is the easier way of prediction in time series, but first order Wiener predictive filter is an enhancement of pure persistence model that have been adopted as the reference model for wind power forecasting. Pure enhanced persistence is simple but hard to beat in short-term prediction. This paper shows some experiments that have been performed by applying the standard protocols with Feed Forward and Recurrent Neural Networks architectures in the background of the requirements for Open Electricity Markets.
URI: http://hdl.handle.net/10553/42866
ISBN: 978-3-642-02477-1
3642024777
ISSN: 0302-9743
DOI: 10.1007/978-3-642-02478-8_161
Source: Cabestany J., Sandoval F., Prieto A., Corchado J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg
Appears in Collections:Actas de congresos
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